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Predict drug adverse effects with Artificial Intelligence, increasing patient health and satisfaction.
Every year, adverse drug reactions (ADRs) cause significant harm in healthcare settings, resulting in more than 750,000 inpatient injuries or deaths, affecting nearly two million hospital stays, and leading to over one million emergency department visits and 125,000 hospitalizations (1)(2). Older adults are particularly vulnerable, experiencing double the incidence of ADRs compared to younger populations and facing a threefold higher risk of mortality from these events. Additionally, between 20% to 60% of older adults use potentially inappropriate medications (PIMs), highlighting the pervasive risks associated with medication management in this demographic (3)(4).
A predictive AI model, "SafeMed AI", has been developed to specifically assess the risk level of ADRs in patients, using a synthetic data set that takes into account patient demographics and health history to measure the low, medium or high risk ADR probability, with the ultimate goal of improving patient safety and treatment outcomes.
The model dataset was structured to reflect clinical realities, based on research on ADRs in older adults by Nair et al. (5), analysis of spontaneous reports by Dubrall et al. (6), impact studies of medication continuation by Weir et al. (7), Pharmacogenetic risk factor piloting by Finkelstein et al. (8), medication appropriateness reviews by Fick (9), and intervention meta-analyses by Gray et al. (10). Using variables such as age, liver and kidney function, and number of medications, the model simulates patient profiles to help healthcare providers personalize treatment to reduce ADR risks.
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